Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.     

Comparative study on economic contribution rate of education of China and foreign countries based on soft computing method

Economic contribution rate of education (ECRE) is the key factor of education economics. This article selected China, South Korea, United States and other countries for a total of 15 samples, and put the data of the same period under the framework of soft computing, to simulate the production chain of “education–potential human capital–actual human capital–economic growth”. The basic idea is: Firstly, 15 countries are softly categorized according to the level of science and technology (S&T) progress. Secondly, potential human capital and actual human capital establish the internal correlation (fuzzy mapping) in the same classification, and we conceptualize actual human capital as one production factor, joined with the other two production factors, fixed asset and land, to set up the fuzzy mapping to economic growth., and then calculate economic contribution rate of education of China and foreign by two fuzzy mapping of them. Thirdly, this paper analyzes the present state and differences in the development of education between China and foreign according to different ECRE, and offers proposals for promoting the education in China.     

Interactive genetic algorithms with large population and semi-supervised learning

Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.     

Birman–Wenzl–Murakami algebra, topological parameter and Berry phase

In this paper, a 3?×?3-matrix representation of Birman?CWenzl?CMurakami (BWM) algebra has been presented. Based on which, unitary matrices A(??, ?? ₁, ?? ₂) and B(??, ?? ₁, ?? ₂) are generated via Yang?CBaxterization approach. A Hamiltonian is constructed from the unitary B(??, ??) matrix. Then we study Berry phase of the Yang?CBaxter system, and obtain the relationship between topological parameter and Berry phase.     

Minimal-drift heading measurement using a MEMS gyro for mobile robots: Fused with odometry

To achieve the goal of low-cost MEMS gyros for the precise self-localization of mobile robots, this paper presents a simple, yet effective method to minimize drifts on the heading angle by combining measurements from a gyro with measurements from wheel encoders (odometry). The main idea of the proposed approach is to estimate the accuracy of both sensors as a function of the actual maneuver being carried out, and then the output of both sensors are fused by the complementary filter taking into account the maneuvering conditions. The proposed method is applied to a mobile robot and the experimental data demonstrate the effectiveness of this approach.     

A fuzzy cerebellar model articulation controller based visual servo system for robot

This paper presents a novel online learning visual servo controller integrating the FCMAC with proportion controller for the control of position of manipulator end-effector. Since the FCMAC has good learning capability and fast learning speed, and can save much computer memory space by fuzzy processing of input space division and memory unit activation, it is used to develop an adaptive control law by learning the relationship between the image feature errors and manipulator input, and the aim of online learning of the FCMAC is to minimize the output of proportion controller. Furthermore, the FCMAC has no need for models of robot manipulator and image feature extraction, so that the capability of proposed controller for tasks under uncertain environment can be improved. Finally, the proposed controller is proved to be effective by the experiment, and compared with BP neural network.     

A vertical and floor line-based monocular SLAM system for corridor environments

In this paper, we propose a vertical and floor line-based monocular simultaneous localization and mapping (SLAM) system which utilizes vertical lines, floor lines, and vanishing points as sensory input to perform robust SLAM in corridor environments. By combining three map feature types, our design can help a robot to perform accurate pose estimation, repeatable loop closure, and to construct a more expressive environmental map. As a primitive element of a geometric structure, a line segment has one additional dimension compared to a point feature, thereby allowing the use of line segments to easily represent a geometric structure using a smaller number of features. This system presents map features on a 2D ground space: the vertical line as a projection point, the floor line as the original line, and the vanishing point as a directional vector. Although the vertical line, floor line, and vanishing point use different parameterization and initialization methods, their measurement models are integrated into a unified extended Kalman filter (EKF) framework. Experimental results show that our system can be deployed in a structured indoor environment as a suitable SLAM solution.     

Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm

A tracking algorithm for identification of vessel contours in digital coronary arteriograms was developed and validated. Given an initial start-of-search point, the tracking process was fully automated by utilizing the spatial continuity of the vessel's centerline, orientation, diameter, and density. The incremental sections along a major vessel were sequentially identified, based on the assumptions of geometric similarity and continuation between adjacent incremental sections. The algorithm consisted of an extrapolation-update process which was guided by a matched filter. The filter parameters were adapted to the measured lumen width. The tracking process was robust and extremely efficient as indicated by test results on synthetic images, digital subtraction angiograms, and cineangiograms. The algorithm provided accurate measurement of lumen width and percent stenosis that was relatively invariant to the vessel's orientation, dynamic range, background variation, and degree of blurring     

A new mechanism of ultrasonic absorption in aqueous solutions of rigid macromolecules

The temperature- and frequency-dependences of the ultrasonic absorption in lyotropic liquid crystals of rod-like FD virus particles have been measured with the ultrasonic beam being parallel or perpendicular to the rod axis. The experimental results show a strongly anisotropic ultrasonic absorption in the lyotropic liquid crystals, which varies with temperature exactly as the shear viscosity of water. Experimental results are in good agreement with the following model for the absorption process. The authors believe that the absorption arises from differential flow of water between the relatively long and rigid rod-like macromolecules. This mechanism of ultrasonic absorption becomes dominant for frequencies of 1-100 MHz. The validity of this mechanism is, however, not restricted to the special lyotropic crystals discussed here. Instead, this absorption process is expected to be important also for other macromolecular solutions whenever the dissolved macromolecular objects have a sufficiently large linear dimension in the direction parallel to the sound beam.     

GemstoneFire: adaptive dispersive ray tracing of polyhedrons

Synthesizing realistic images of gemstones requires techniques beyond the scope of normal ray tracing. The fire of such highly refractive objects is what makes gemstones attractive, and also imposes very high computational overhead to perform time consuming dispersive ray tracing. Gemstones are usually cut in polyhdrons as for example, a brillant cut. After a detailed analysis of the nature of dispersive ray tracing of polyhedral objects, we propose here a new method of using three simple rays adaptively to model the ray spreading caused by dispersive refraction. It is shown that the proposed method reduces the computational complexity to an order close to that of normal ray tracing.